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Postdoctoral Fellow Machine Learning Jobs in Pittsburgh, PA

Fujitsu

Senior Researcher, AI Security

Pittsburgh, PA · On-site

We are seeking a highly motivated and talented research scientist working in machine learning (ML ... postdoctoral research or impactful projects. * Proficiency in ML and AI programming written in ...

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Postdoctoral Fellow Machine Learning information

See Pittsburgh, PA salary details

$24.3K

$57.3K

$81.1K

How much do postdoctoral fellow machine learning jobs pay per year?

As of Sep 2, 2026, the average yearly pay for postdoctoral fellow machine learning in Pittsburgh, PA is $57,299.00, according to ZipRecruiter salary data. Most workers in this role earn between $47,600.00 and $64,600.00 per year, depending on experience, location, and employer.

What is a postdoctoral fellow in machine learning?

A Postdoctoral Fellow in Machine Learning is a researcher who has recently completed their PhD and is engaged in advanced research in the field of machine learning. This role typically involves conducting independent or collaborative research, publishing scientific papers, and sometimes mentoring students. Postdoctoral fellows often work at universities, research institutes, or industry labs, focusing on developing new algorithms, improving existing models, or applying machine learning techniques to specific problems. The position is usually temporary, lasting one to three years, and aims to prepare researchers for permanent academic or industry roles.

What are the key skills and qualifications needed to thrive as a postdoctoral fellow in machine learning?

To thrive as a Postdoctoral Fellow in Machine Learning, you need a strong background in computer science, mathematics, and statistics, typically supported by a PhD and relevant research experience. Familiarity with programming languages such as Python, machine learning frameworks like TensorFlow or PyTorch, and experience in high-performance computing environments are commonly required. Strong analytical thinking, effective scientific communication, and collaboration skills help you contribute to research teams and disseminate findings. These skills and qualities are crucial for advancing research, developing innovative solutions, and building a successful academic or industry career in machine learning.

What are some common challenges faced by postdoctoral fellows in machine learning, and how can they be addressed?

Postdoctoral Fellows in Machine Learning often encounter challenges such as balancing independent research with collaborative projects, staying current with rapidly evolving technologies, and securing funding or publishing in top-tier journals. To address these, it's helpful to establish clear communication with mentors and collaborators, set aside dedicated time for reading recent literature, and actively seek feedback on research drafts. Building a professional network through conferences and seminars can also open opportunities for collaboration and career advancement.

What is the difference between Postdoctoral Fellow Machine Learning vs Postdoctoral Research Scientist?

AspectPostdoctoral Fellow Machine LearningPostdoctoral Research Scientist
Required credentialsPhD in Computer Science, Data Science, or related fieldPhD in relevant field, often with specialized research experience
Work environmentAcademic labs, universities, research institutionsResearch labs, industry R&D departments, tech companies
Employer and industry usagePrimarily academia, government researchPrimarily industry, corporate research divisions
Common search and comparison intentUnderstanding academic research roles in machine learningExploring industry-focused research career paths

Postdoctoral Fellow Machine Learning roles typically focus on academic research, requiring a PhD and working in universities or research institutions. In contrast, Postdoctoral Research Scientist positions are often industry-based, emphasizing applied research within corporate R&D departments. Both roles involve advanced machine learning expertise but differ mainly in work environment and career trajectory.

What are popular job titles related to Postdoctoral Fellow Machine Learning jobs in Pittsburgh, PA?

For Postdoctoral Fellow Machine Learning jobs in Pittsburgh, PA, the most frequently searched job titles are:

What job categories do people searching Postdoctoral Fellow Machine Learning jobs in Pittsburgh, PA look for?

The top searched job categories for Postdoctoral Fellow Machine Learning jobs in Pittsburgh, PA are:

What cities near Pittsburgh, PA are hiring for Postdoctoral Fellow Machine Learning jobs?

Cities near Pittsburgh, PA with the most Postdoctoral Fellow Machine Learning job openings:

Postdoctoral Fellow Machine Learning jobs near you

Post Doctoral.Post Doctoral.Associate

University of Pittsburgh

Pittsburgh, PA • On-site

$47K - $64K/yr

Full-time

Re-posted 7 days ago

Job description

Postdoctoral Associate in AI-Driven Omics Analysis and Drug Discovery at the Vascular Medicine Institute, Department of Medicine, School of Medicine, University of Pittsburgh 

The Vascular Medicine Institute at the University of Pittsburgh is seeking a highly motivated postdoctoral associate to join a computational research program focused on AI-driven omics analysis, systems biology, and therapeutic discovery. The successful candidate will develop and apply computational and deep learning approaches to understand how complex biological stressors drive molecular, cellular, and organ dysfunction, and to identify therapeutic strategies for disease treatments. The project will involve large-scale analysis and integration of transcriptomics, single-cell RNA-seq, and other omics modalities.

Our research group operates at the intersection of computational biology, systems medicine, and translational science within the Vascular Medicine Institute. We focus on integrating large-scale omics data with mechanistic modeling to uncover systemic drivers of disease. This position provides a unique opportunity to develop broadly applicable computational frameworks for understanding organ dysfunction and therapeutic intervention.

Major duties:

  • Develop and apply AI/deep learning models for omics-based disease mechanism discovery and therapeutic prediction.
  • Analyze and integrate high-throughput omics datasets, including bulk RNA-seq, single-cell/nuclei RNA-seq, epigenomics, proteomics, metabolomics, and genomics.
  • Develop and refine drug-repurposing and target-prioritization algorithms using disease signatures, perturbation datasets, signaling networks, and drug-target databases.
  • Build computational pipelines for preprocessing, quality control, harmonization, and integration of public and internal omics datasets.

Minimum Requirements for the candidate:

  • PhD in statistical learning, computational biology, systems biology, data science, or a related quantitative discipline.
  • Fluency in Python programming language
  • Experience with high-throughput omics data analysis
  • Strong foundation in statistics, machine learning, mathematics, and biological data interpretation

Interested applicants should apply via join.pitt.edu Requisition #26003687

Apply